Sentence similarity is used to check how similar (or dissimilar) two sentences or passages are. From plagiarism checking to information retrieval, sentence similarity has a lot of uses.

Find text similarity using the API

The sentence-transformers/all-MiniLM-L6-v2 model is recommended for text similarity tasks. However, there are many models available for this task, and some common models are below:

Models for Text Similarity

Model	Description
`sentence-transformers/all-MiniLM-L6-v2`	Based on `nreimers/MiniLM-L6-H384-uncased` and fine-tuned on multiple datasets. Finds the semantic similarity between the input sentences.
`sentence-transformers/all-mpnet-base-v2`	Based on `microsoft/mpnet-base` and trained on multiple datasets, which contain around one billion sentences. Also used for semantic search.
`sentence-transformers/multi-qa-MiniLM-L6-cos-v1`	Based on `nreimers/MiniLM-L6-H384-uncased` and fine-tuned on multiple datasets. Finds the semantic similarity between the input sentences.

Parameter	Type	Category	Description
`inputs.source_sentence`	String	Required	Specifies the source sentence/passage to compare with other `inputs.sentences`
`inputs.sentences`	String	Required	Specifies the list of sentences that will be compared with `source_sentence`
`options.use_cache`	Boolean	Optional	Hugging Face Inference API has a cache mechanism implemented to speed up the requests. Use it for the deterministic models. Default value is `true`.
`options.wait_for_model`	Boolean	Optional	Hugging Face Inference API models takes time to initiate and process the requests. If the value is `true`, it waits for the model to get ready instead of returning an error. Default value is `false`.

Javascript (babel-node)

// Endpoint URL
const endpointUrl = "https://api-inference.huggingface.co/models/sentence-transformers/all-MiniLM-L6-v2";
const headerParameters = {
  "Authorization": "Bearer {{ACCESS_TOKEN}}"
};
// Input text to classify
const data = JSON.stringify({
    inputs: {
        "source_sentence": "What's up with the weather?",
        "sentences": ["Global warming's effects are getting serious.", "Good morning, be positive and good things will happen."]
    },
    options: {
      wait_for_model: true
    }
});
const options = {
  method: "POST",
  headers: headerParameters,
  body: data
};
async function textSimilarity() {
   try {
    const response = await fetch(endpointUrl, options);
    printResponse(response);
  } catch (error) {
    printError(error);
  }
}
textSimilarity();

Let’s have a look at the highlighted lines shown in the code widget above:

Line 2: We specify the sentence-transformers/all-MiniLM-L6-v2 model for text similarity.
Lines 10–11: We set the source_sentence to compare it with the sentences.
Lines 24–31: We create a function, textSimilarity, to make the API call and handle the exceptions.
Line 33: We call the textSimilarity function to invoke the endpoint.

Response fields

The API call above returns a list of scores depending on the inputs, and these are the semantic similarity scores of the source_sentence with each sentence in the sentences list. We have two sentences to find the similarity with source_sentence, and the score of the first sentence will be higher because it talks about the weather conditions, which are related to the source_sentence.

Example

Try out the following example in the widget above, and observe which sentence has a high score:

1.Introduction

2.NLP

3.Computer Vision

4.APIs Integration in React

5.Conclusion

Text Similarity

Find text similarity using the API

Models for Text Similarity

Request parameters

Response fields

Example